ArticleJournal of the American Medical Informatics Association : JAMIA2015
Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- A high-precision hierarchical registration approach for stain- and scanner-independent colocalization on whole slide images in histopathology.Health information science and systems · 2025Article
- Advancement of Techniques for Precise Visualization and Quantification of Tertiary Lymphoid Structure-Associated Immune Cells in Tissue Samples.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges.British journal of cancer · 2023Review
- Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis.Scientific reports · 2022Article
- Regional registration of whole slide image stacks containing major histological artifacts.BMC bioinformatics · 2020Article
- Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps.Entropy (Basel, Switzerland) · 2020Article
- Article
- Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring.Transplantation · 2019Review
- Hyper-Stain Inspector: A Framework for Robust Registration and Localised Co-Expression Analysis of Multiple Whole-Slide Images of Serial Histology Sections.Scientific reports · 2017Article
- A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.PloS one · 2015Article
- Under the (Light) Sheet After the iDISCO.Epilepsy currentsArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveExtracting accurate information from complex biological processes involved in diseases, such as cancers, requires the simultaneous targeting of multiple proteins and locating their respective expression in tissue samples. This information can be collected by imaging and registering adjacent sections from the same tissue sample and stained by immunohistochemistry (IHC). Registration accuracy should be on the scale of a few cells to enable protein colocalization to be assessed.
methodsWe propose a simple and efficient method based on the open-source elastix framework to register virtual slides of adjacent sections from the same tissue sample. We characterize registration accuracies for different types of tissue and IHC staining.
resultsOur results indicate that this technique is suitable for the evaluation of the colocalization of biomarkers on the scale of a few cells. We also show that using this technique in conjunction with a sequential IHC labeling and erasing technique offers improved registration accuracies. DISCUSSION: Brightfield IHC enables to address the problem of large series of tissue samples, which are usually required in clinical research. However, this approach, which is simple at the tissue processing level, requires challenging image analysis processes, such as accurate registration, to view and extract the protein colocalization information.
conclusionsThe method proposed in this work enables accurate registration (on the scale of a few cells) of virtual slides of adjacent tissue sections on which the expression of different proteins is evidenced by standard IHC. Furthermore, combining our method with a sequential labeling and erasing technique enables cell-scale colocalization.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.